Local one-dimensional embedding interpolation for hyperspectral image classification

Abstract

In the hyperspectral image classification area, a few number of labeled samples is a bottleneck for the improvement of classification accuracy. In order to tackle this problem, multiple one-dimensional embedding interpolation (M1DEI) has been used for hyperspectral image classification and achieved promising results. Despite the success, the complexity of M1DEI prevents its practical application. On the other hand, the percentage of newly added samples is set by experience when enlarging the labeled set. In this paper we develop a method by extending the M1DEI method with local strategy, called multiple local one-dimensional embedding interpolation (ML1DEI). We only map the labeled samples and their local spatial neighbors into the one-dimensional (1D) space. The local strategy can reduce the complexity of M1DEI, since only labeled samples and their neighbors need to be mapped. In addition, the local strategy ensures all these newly labeled samples come from the spatial neighborhood of labeled samples. Then, during the merging stage, we can incorporate all of them with the labeled samples. Moreover, the proposed ML1DEI can incorporate the spatial information and make full use of the unlabeled samples. Compared with other spatial-spectral classification methods, the proposed ML1DEI method obtains promising results. Experimental results on the commonly used hyperspectral data set validate the effectiveness of the proposed method.